TRA CỨU
thông tin biểu ghi
ISSN 2409-0026
DDC 616
Tác giả CN Tran, Cao Minh
Nhan đề UGGNet : Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis / Tran Cao Minh, Nguyen Kim Quoc, Phan Cong Vinh, Dang Nhu Phu, [...]
Thông tin xuất bản DOAJ, 2024
Mô tả vật lý 8 tr. : picture, tables ; 24 cm.
Tóm tắt In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for the early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast Ultrasound Images Dataset."
Từ khóa tự do Breast Cancer
Từ khóa tự do Classification
Từ khóa tự do Deep Learning
Khoa Khoa Y
Tác giả(bs) CN Nguyen, Kim Quoc
Tác giả(bs) CN Dang, Nhu Phu
Tác giả(bs) CN Phan, Cong Vinh
Nguồn trích EAI Endorsed Transactions on Contex-aware Systems and Applications. ISSN: 2409-0026, , 10
Địa chỉ Thư Viện Đại học Nguyễn Tất Thành
000 00000nam#a2200000u##4500
00156308
00212
004E776F656-704C-42F8-A4AD-BBB3070805A0
005202507281007
008250728s2024 vm vie
0091 0
022 |a2409-0026
039|y20250728100718|ztainguyendientu
040 |aNTT
041 |avie
044 |avm
082 |a616|bT685|223
100 |aTran, Cao Minh
245 |aUGGNet : |bBridging U-Net and VGG for Advanced Breast Cancer Diagnosis / |cTran Cao Minh, Nguyen Kim Quoc, Phan Cong Vinh, Dang Nhu Phu, [...]
260 |bDOAJ, |c2024
300 |a8 tr. : |bpicture, tables ; |c24 cm.
520 |aIn the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for the early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast Ultrasound Images Dataset."
653 |aBreast Cancer
653 |aClassification
653 |aDeep Learning
690 |aKhoa Y
700 |aNguyen, Kim Quoc
700 |aDang, Nhu Phu
700 |aPhan, Cong Vinh
773 |tEAI Endorsed Transactions on Contex-aware Systems and Applications|x2409-0026|i10
852 |aThư Viện Đại học Nguyễn Tất Thành
890|a0|b0|c1|d0
Không tìm thấy biểu ghi nào